2024-01-30 09:46:40 +01:00
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#%reset
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2024-01-25 17:21:18 +01:00
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import pm4py
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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from python_helpers import eval_pm, pn_infos
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###### Load data and create event logs ######
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dat = pd.read_csv("results/haum/event_logfiles_2024-01-18_09-58-52.csv", sep = ";")
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dat = dat[dat["date.start"] < "2020-03-13"]
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dat = dat[dat["path"] != 106098] # exclude broken trace
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# --> only pre corona (before artworks were updated)
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log_path = pm4py.format_dataframe(dat, case_id = "path", activity_key = "event",
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timestamp_key = "date.start")
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###### Infos for items ######
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mdi = pd.DataFrame(columns = ["fitness", "precision", "generalizability",
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"simplicity", "sound", "narcs", "ntrans",
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"nplaces", "nvariants", "mostfreq"])
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for item in log_path.item.unique().tolist():
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mdi = pd.concat([mdi, pn_infos(log_path, "item", item)])
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mdi = mdi.sort_index()
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# Export
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mdi.to_csv("results/haum/pn_infos_items.csv", sep = ";")
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# datitem = dat.groupby("item")[["duration", "distance",
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# "scaleSize", "rotationDegree"]].mean()
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#
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# def length_path(data):
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# x = data.path
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# return len(x.unique())
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# def length_case(data):
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# x = data.case
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# return len(x.unique())
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# def length_topic(data):
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# x = data.topic.dropna()
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# return len(x.unique())
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#
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# datitem["npaths"] = dat.groupby(["item"]).apply(length_path)
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# datitem["ncases"] = dat.groupby(["item"]).apply(length_case)
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# datitem["ntopics"] = dat.groupby(["item"]).apply(length_topic)
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#
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# datitem.index = datitem.index.astype(str).str.rjust(3, "0")
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# datitem = datitem.sort_index()
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# datitem.index = mdi.index
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#
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# datitem = pd.concat([mdi, datitem], yaxis = 1)
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